https://github.com/bruin-data/ingestr
ingestr is a CLI tool to copy data between any databases with a single command seamlessly.
https://github.com/bruin-data/ingestr
bigquery copy-database data-ingestion data-integration data-pipeline duckdb ingestion-pipeline mssql postgresql snowflake
Last synced: 8 days ago
JSON representation
ingestr is a CLI tool to copy data between any databases with a single command seamlessly.
- Host: GitHub
- URL: https://github.com/bruin-data/ingestr
- Owner: bruin-data
- License: mit
- Created: 2024-02-12T23:00:36.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-09T15:06:34.000Z (12 days ago)
- Last Synced: 2025-05-09T15:27:11.828Z (12 days ago)
- Topics: bigquery, copy-database, data-ingestion, data-integration, data-pipeline, duckdb, ingestion-pipeline, mssql, postgresql, snowflake
- Language: Python
- Homepage: https://bruin-data.github.io/ingestr/
- Size: 167 MB
- Stars: 2,951
- Watchers: 20
- Forks: 79
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
- Support: docs/supported-sources/adjust.md
Awesome Lists containing this project
- awesome-github-repos - bruin-data/ingestr - ingestr is a CLI tool to copy data between any databases with a single command seamlessly. (Python)
- awesome-list - bruin-data/ingestr - ingestr is a CLI tool to copy data between any databases with a single command seamlessly. (Python)
README
Copy data from any source to any destination without any code
---
ingestr is a command-line app that allows you to ingest data from any source into any destination using simple command-line flags, no code necessary.
- ✨ copy data from your database into any destination
- ➕ incremental loading: `append`, `merge` or `delete+insert`
- 🐍 single-command installation
ingestr takes away the complexity of managing any backend or writing any code for ingesting data, simply run the command and watch the data land on its destination.
## Installation
We recommend using [uv](https://github.com/astral-sh/uv) to run `ingestr`.
```
pip install uv
uvx ingestr
```
Alternatively, if you'd like to install it globally:
```
uv pip install --system ingestr
```
While installation with vanilla `pip` is possible, it's an order of magnitude slower.
## Quickstart
```bash
ingestr ingest \
--source-uri 'postgresql://admin:admin@localhost:8837/web?sslmode=disable' \
--source-table 'public.some_data' \
--dest-uri 'bigquery://?credentials_path=/path/to/service/account.json' \
--dest-table 'ingestr.some_data'
```
That's it.
This command:
- gets the table `public.some_data` from the Postgres instance.
- uploads this data to your BigQuery warehouse under the schema `ingestr` and table `some_data`.
## Documentation
You can see the full documentation [here](https://bruin-data.github.io/ingestr/getting-started/quickstart.html).
## Community
Join our Slack community [here](https://join.slack.com/t/bruindatacommunity/shared_invite/zt-2dl2i8foy-bVsuMUauHeN9M2laVm3ZVg).
## Contributing
Pull requests are welcome. However, please open an issue first to discuss what you would like to change. We maybe able to offer you help and feedback regarding any changes you would like to make.
> [!NOTE]
> After cloning `ingestr` make sure to run `make setup` to install githooks.
## Supported sources & destinations
Source
Destination
Databases
BigQuery
✅
✅
ClickHouse
✅
✅
Databricks
✅
✅
DuckDB
✅
✅
DynamoDB
✅
-
Local CSV file
✅
✅
Microsoft SQL Server
✅
✅
MongoDB
✅
❌
MySQL
✅
❌
Oracle
✅
❌
Postgres
✅
✅
Redshift
✅
✅
SAP Hana
✅
❌
Snowflake
✅
✅
SQLite
✅
❌
Platforms
Adjust
✅
-
Airtable
✅
-
Amazon Kinesis
✅
-
Apache Kafka
✅
-
AppsFlyer
✅
-
App Store
✅
-
Asana
✅
-
Chess.com
✅
-
Facebook Ads
✅
-
Github
✅
-
Gorgias
✅
-
Google Sheets
✅
-
Google Ads
✅
-
Google Analytics
✅
-
Klaviyo
✅
-
LinkedIn Ads
✅
-
Notion
✅
-
Personio
✅
-
S3
✅
-
Salesforce
✅
-
Shopify
✅
-
Slack
✅
-
Stripe
✅
-
TikTok Ads
✅
-
Zendesk
✅
-
Feel free to create an issue if you'd like to see support for another source or destination.
## Acknowledgements
This project would not have been possible without the amazing work done by the [SQLAlchemy](https://www.sqlalchemy.org/) and [dlt](https://dlthub.com/) teams. We relied on their work to connect to various sources and destinations, and built `ingestr` as a simple, opinionated wrapper around their work.